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Journal of Finance and Accountancy Volume 16 – September, 2014

Factors associated with student, page 1

Factors associated with student performance in an investments

course: An empirical study

Keshav Gupta

Kutztown University of Pennsylvania

Mostafa M. Maksy

Kutztown University of Pennsylvania

ABSTRACT

Using several statistical tests (including one-way ANOAVA, Pearson and Spearman

correlations, and OLS regression analysis) this paper examines some determinants of student

performance in an undergraduate Investments course. Of the three motivation factors studied

(the grade the student would like to make in the course, intention to take the Chartered Financial

Analyst or the Certified Financial Planner examination, and intention to attend graduate school)

only the first has strong relationship with student performance. Of the effort factors (course study

hours, overall study hours, homework, class attendance, and class participation) only course

study hours, homework, and attendance have positive explanatory power for student

performance. None of the three distraction factors studied (job hours, job type, and credit hours

load for the semester) has any significant effect on student performance. Both prior ability

factors studied (overall GPA and the grade in a pre-requisite financial management course) have

significant relationship with student performance. Finally, of the four self-perceived ability

factors used in the study (writing, math, reading, and listening) only the math ability has positive

relationship with student performance.

Keywords: Motivation, Effort, Distraction, Prior ability, Student performance, Investments.

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141846 – Journal of Finance and Accountancy

Factors associated with Student 2

INTRODUCTION

A review of prior research below indicates, very few studies have investigated the impact

of motivation and effort on a required Financial Management course. No studies were identified

in this study that looked at determinants of success in an Investments course which is required

for most finance majors and taken by many non-finance majors as an elective. This study

investigates the associations between selected motivation, effort, distraction, and prior ability

factors and student performance in the undergraduate Investments course.

The grade the students would like to make in the course, intention to take the Chartered

Financial Analyst (CFA) or the Certified Financial Planner (CFP) examination, and intention to

attend graduate school were used as proxies for motivation. The number of weekly study hours

for the course, homework grade, class attendance, and class participation grade were used as

proxies for effort. The number of hours of work per week, and the number of credit hours taken

per semester were used as proxies for distraction.

Students’ prior ability is measured by the actual grade earned in the Financial

Management course which is a pre-requisite for the Investment course, overall Grade Point

Average (OGPA), and self-reported math ability and combined writing, reading and listening

abilities. The dependent variable, the student performance, is measured three different ways; by

the letter grade for the course, percent score for the course and by the percent score in tests given

in class.

One of the motivations of this study is predicated on the belief that identifying effort

factors that help students to perform well and factors that distract them from performing well

may help us emphasize the effort factors and discourage the distraction factors. Another purpose

of the study is to provide empirical support to the intuitive notion that motivation does indeed

lead to better student performance. Also the study could help us determine whether students’

self-assessment of their own writing, math, reading, and listening abilities affect their

performance in the course.

REVIEW OF PRIOR RESEARCH

Prior studies have explored various factors (e.g., general academic performance, aptitude,

prior exposure to mathematics, prior exposure to accounting, gender, age, motivation, effort, and

other intervening variables) that are associated with student performance in college-level

courses. The overall Grade Point Average (OGPA) is used frequently as a proxy for prior

academic performance and aptitude.

In the finance area, Paulsen and Gentry (1995), Chan, Shum, and Wright (1997), Sen,

Joyce, Farrel, and Toutant (1997), Didia and Hasnat (1998), Marks (1998), Van Ness, Van Ness,

and Adkins (2000), Johnson, Joyce, and Sen (2002), Biktimirov and Klassen (2008), find OGPA

to be a strong predictor of grade in the Financial Management course that is required of all

business majors. Several researchers, using data from various U.S. colleges, find evidence

supporting OGPA as a significant predictor of performance in accounting courses (Eckel and

Johnson 1983; Hicks and Richardson 1984; Ingram and Peterson 1987; Eskew and Faley 1988;

Doran, Bouillon, and Smith 1991). Wooten (1998) finds that aptitude, as measured by the

Scholastic Aptitude Test (SAT) score, and grade history are significant variables in influencing

performance of students in an introductory accounting course. U.S. research findings “are

supported in Australia by Jackling and Anderson (1998) and in Scotland by Duff (2004). In

Wales, Lane and Porch (2002) find that, performance in introductory accounting can partially be

explained by reference to factors in the students’ pre-university background. However, these

factors are not significant when the student progresses to upper level accounting classes. In



addition, using another measure, pre-university examination performance, Gist, Goedde, and

Ward (1996) find no significant association between academic performance and performance in

accounting courses at the university level. Finance and accounting are subject areas that require

accumulation of prior knowledge and quantitative skills. Thus, several studies have investigated

the impact of prior exposure to mathematics and accounting on performance in college finance

and accounting courses” (Masky, et al, 2013)..

With regard to Financial Management courses, the evidence is mixed. While Chan,

Shum and Wright (1997) show that self-reported quantitative skills have insignificant impact on

students’ course score, Grover, Heck and Heck (2010) report significant explanatory power for

pre-test math, accounting and economics scores. Didia and Hasnat (1998) find mixed results

with math grade being significant predictor of course grade for OLS model but not for the

ordered-probit model. However, they find strong evidence, using both OLS and ordered-probit

estimates, that grades in accounting and economics pre-requisite courses have predictive value

for the Financial Management course. Sen, Joyce, Farrell and Toutant (1997) also find positive

relationship between completion of pre-requisites and performance in the Financial Management

course.

Financial management pre-requisites almost always include two accounting courses. In

the accounting area, “the results are also inconclusive. On one hand, some studies (for example,

Baldwin and Howe 1982; Bergin 1983; and Schroeder 1986) find that performance is not

significantly associated with prior exposure to high school accounting education. On the other

hand, some later studies (for example, Eskew and Faley 1988; Bartlett et al 1993; Gul and Fong

1993; Tho 1994; Rohde and Kavanagh 1996) find that prior accounting knowledge, obtained

through high school education, is a significant determinant of performance in college-level

accounting courses. There is also some ambiguity with regard to the influence of mathematical

background on performance in accounting courses. For example, Eskew and Faley (1988) and

Gul and Fong (1993) suggest that students with strong mathematical backgrounds outperform

students with weaker mathematical backgrounds. On the other hand, Gist et al (1996) do not

report the same results. Furthermore, Guney (2009) suggests that grades in secondary education

mathematics are a very strong determinant of performance in accounting but only for non-

accounting majors” (Masky, et al, 2013).

Prior studies about the influence of motivation and effort on student performance also

report conflicting results. For example, Pascarella and Terenzini (1991), report that motivation

and effort, among other factors, significantly influence students’ performance in college.

Wooten (1998) finds that motivation significantly affects effort which in turn significantly

affects performance in an introductory accounting course. Maksy and Zheng (2008) use “the

grade the student would like to earn” as a proxy for motivation and find it to be significantly

associated with the student’s performance in advanced accounting and auditing courses. Paulsen

and Gentry (1995), using a survey instrument, report that students’ academic performance in a

large introductory Financial Management course was significantly related to several motivational

variables such as intrinsic and extrinsic goal orientations and task value, and learning strategy

variables, including time, study, and effort. Johnson, Joyce and Sen (2002) utilize computerized

quizzes and analyze the effect of objectively measured effort on student performance in

Financial Management course. They show that, after controlling for aptitude, ability, and gender,

effort as measured by attempts and log time, remains significant in explaining the differences in

performance. Rich (2006), uses students’ homework preparedness and unpreparedness in class as

a proxy of effort and non-effort. He finds significant positive relationship for the former and

negative relationship for the latter with exam percent. Biktimirov and Klassen (2008) find weak

association between hits to course management system and grade in finance course. However,

using self-reported data, Didia and Hasnat (1998) present very week counter-intuitive evidence



for one of the two OLS models but not for the ordered-probit models that the more time spent

studying per week the lower the grade in the introductory finance course. However, they did not

control for GPA. Also, using self-reported data, Nofsinger and Petry (1999) find no significant

relationship between effort and performance in a Principles of Finance course.

In recent years, there has been increased interest in studying the influence of intervening

variables on student performance. Paulsen and Gentry (1995), using a survey instrument, find

that academic performance in a large introductory financial management class is significantly

related to control over learning, test anxiety, self-efficacy, elaboration, organization and

metacognition. Wooten (1998) finds no significant relationship between work, family, and

extra-curricular conflicts and students’ performance in an introduction to accounting course.

Chan, Shum, and Wright (1997) find no significant relationship between performance in a

financial management course and attendance, credit hours enrolled, and number of weekly work

hours. In a similar vein, Van Ness et al (2000) find no relationship between students’ full time or

part time status and grades in a Principles of Finance class. However, they find that students

who are enrolled in internet class are more likely not to complete the course. This appears to be

contrary to Paulsen and Gentry finding because the internet course is designed to give students

more control over their learning in terms of very flexible deadline for assignments and one full

year to complete the course,. Didia and Hasnat (1998) find strong positive relationship between

number of credit hours enrolled in the semester and course grades. This result may seem to be

counter intuitive; however, some research, including this study, shows that students with higher

GPAs take more credit hours. Rich (2006) reports significant negative relationship between

class absences and being late to the class, and exam percent. In the accounting area, Wooten

(1998) does not find significant relationship between course performance and work, family, and

extracurricular conflicts. “Paisey and Paisey (2004) and Guney (2009) show there is a clear

positive relationship between attendance and academic performance. Paisey and Paisey also

report that the most frequently cited reason for not attending classes was students’ participation

in part-time employment. Similarly, Lynn and Robinson-Backmon (2005) find a significant

adverse association between employment status and learning outcomes. These authors also

indicate that a student’s self-assessment of course learning objectives is significantly and directly

related to grade performance. In contrast, Maksy and Zheng (2008) find no significant negative

association between the number of hours of work per week and student performance in advanced

accounting and auditing courses” (Masky, et al, 2013).

However, their study was strictly conducted in a commuter school where 80% of the

students worked full time. “Schleifer and Dull (2009) address metacognition in students and find

a strong link between metacognitive attributes and academic performance. Metacognition is

frequently described as “thinking about thinking” and includes knowledge about when and how

to use particular strategies for learning or for problem solving” (Masky, et al, 2013).

Age and gender are two demographic variables that receive less attention than those

factors discussed above, but the results are still inconclusive. Chan, Shum, and Wright (1997),

Didia and Hasnat (1998), and Van Ness et al. (2000) find no significant relationship between

grade in an introductory finance course and gender or age of students. Henebry and Diamond

(1998) and Johnson et al. (2002) also do not find any significant relationship between a finance

principles course score and gender of students. However, Henebry and Diamond show that both

male and female students earn significantly higher grades in courses taught by female instructors.

This difference was not attributable to adjunct, tenure track, or tenured status of instructors. Sen

et al. (1997), on the other hand, show that female student performed worse than male students in

principles of finance courses at two different mid-western universities. In the field of

accounting, Bartlett et al (1993) and Kohl and Kohl (1999) suggest that younger students have

better performance, particularly at the senior university level. However, Jenkins (1998) and Lane



and Porch (2002) conclude that age is not a significant determinant of performance in auditing

and management accounting courses. The studies related to gender also produce conflicting

results. Some studies indicate that male students perform better than female ones, but the results

are either insignificant (for example, Lipe 1989) or only hold true for introductory courses

(Doran, Bouillon and Smith 1991). To the contrary, Mutchler et al (1987) find that female

students score significantly higher than male students. Furthermore, Gracia and Jenkins (2003)

find that there is a significant difference in the performance in favor of female students over male

students in Wales. In contrast, other studies find no significant differences in performance

between male and female accounting students. For example, Tyson (1989) and Buckless et al

(1991) demonstrate that gender effect disappears after controlling for general academic ability.

Similarly, Gammie et al (2003) find very little indication of performance differential between

males and females throughout the degree program.

It is also possible that other intervening variables, besides the demographic variables,

may affect student performance in accounting courses in college. “Bartlett et al (1993) conclude

that very few of the educational, demographic or financial characteristics variables appear to

have a significant influence on student performance in university accounting examinations.

Gracia and Jenkins (2003) observe that students who actively demonstrate commitment and self-

responsibility towards their studies tend to do well in formal assessments. Accordingly, they

agree with Bartlett et al (1993) that intervening variables, rather than demographic variables,

may be important determinants of student performance in university accounting examinations.

They are also in agreement with Lane and Porch (2002) who suggest that other important factors

like student motivation may explain student performance” (Masky, et al, 2013).

There is very limited, almost non-existent, literature on student performance in upper

level finance classes. Dolvin and Pyles (2011) find that trading simulation performance in an

investments class has no significant impact on knowledge level and interest in the discipline or

the investment profession. Huffman (2011) finds that the real estate major status is associated

with higher grade performance in an advanced real-estate course.

Conflicting results are also observed about the association between student performance

in introductory accounting and their performance in non-introductory accounting courses. For

example, Canlar (1986) finds evidence that college-level exposure to accounting is positively

related to student performance in the first MBA-level financial accounting course. Additionally,

Tickell and Smyrnios (2005) find that the best predictor of academic performance in any one

year is the performance in the same discipline in the previous year. Doran et al (1991) report

very surprising and counterintuitive result that performance in the introductory accounting

course has a negative impact on performance in subsequent accounting courses.

Maksy and Zheng (2008) find that the grade in intermediate accounting II is a strong

predictor of student performance in advanced accounting and auditing courses. Research

has been largely inconclusive or replete with conflicting results. The purpose of this study

is to provide more insight on those areas in which there was some general agreement.

Since motivation and effort has generally been positively associated with student

performance affect student performance. The study also looks at several factors which are

commonly viewed as possibly distracting students from performing well and test whether

they indeed are negatively affecting student performance. Moreover, the study

investigates the impact of two specific measures of prior abilities on student performance,

and also use them as control variables while testing for the association between

motivation and distraction factors and student performance in the Investments course

(Maksy & Zheng, 2008)



STUDY OBJECTIVES

The first objective of this paper is to study the relationship between three selected

motivation factors (the grade the student would like to make in the course, the student’s intention

to take the CFA or the CFP examination, and the student’s intention to attend graduate school),

and the student’s performance in the Investments course at a public residential school.

The second objective is to study the relationship between four variables representing

“effort” and student performance. If the students are really motivated, they should spend more

time studying for the course, do well in homework assignments, attend all classes, and

participate in class discussions. The study investigates the possible associations between these

effort variables and motivation factors.

The third objective is to study the relationship between three distraction factors (the

student’s number of working hours per week during the semester, the student’s credit hours

taken in the semester, and the student’s job type, i.e., whether it is finance, accounting, or

business related or not) and the student’s performance in the Investments course. Intuitively, the

higher the number of work hours per week, the less time the student will have to study for the

Investments course resulting in lower course grade. Furthermore, it is likely that the performance

of a student taking higher number of credit hours will be affected negatively because the student

may not be able to devote sufficient number of hours of study to the course. Additionally, if the

student’s job is not related to finance, accounting, or business in general, the student’s grade in

the Investments course will be lower than if the student’s job is related to one of these areas. In

light of the prior discussion, it is hypothesized that if the student’s number of work hours per

week is higher, and/or the number of credits taken in the semester is higher, and/or the student’s

job is not related to finance, accounting, or business in general, there will be a significant

negative impact between these distraction factors and the student’s performance in the

Investments course. Of course, distraction factors may offset each other thereby cancelling out

any single factor’s effect. For example, a student who works higher number of hours per week

may take fewer courses or fewer credit hours, and vice versa, so that there is no negative effect

on performance. For this reason, the study tests the effect of each distraction factor on student

performance while controlling for the other two factors. The association between the distraction

factors among themselves and with the four effort factors is also investigated.

The fourth objective is to study the relationship between students’ performance in the

Investments course and their grade in the financial management course (which is pre-requisite

for the course), their overall GPA, their self-reported ability in math, and in writing, reading, and

listening combined. A positive relationship between self-reported abilities and performance may

indicate that students make reasonably accurate assessment of their abilities. A lack of

relationship between certain abilities and performance could be due to the possibility that those

abilities are not relevant to the performance in the course or to students’ inaccurate assessment of

their abilities. Before the students filled out the questionnaires, they were instructed to be as

honest as possible in their answers so students who plan to take this course in the future would

benefit from the results of this research. It is assumed that the students followed these

instructions and, thus, positive associations between students’ self-perceived abilities and their

performance in the Investments course are expected.

To compare this study with previous studies, whether performance in the Investments

course differs between gender and age groups.is also investigated.

STUDY VARIABLES



The authors initially used only the letter grade in the course (A=4, B=3, etc.) as the student

performance dependent variable. However, they quickly realized that the letter grade treats a

student earning the lowest end of the grade range as having the same exact performance as that

of a student earning the highest end of the grade range. For example, a student with a total

percentage points of 80 and another with a total percentage points of 89 would be considered

having equal performance since both students receive a B for the course, even though the first

student is one percentage point away from a C grade and the other student is one percentage

point away from an A grade (Plus/minus grading was not available). As a result, it was also

decided to use overall points percentage earned by a student in the course as a dependent

variable. Overall points percent score is a weighted score of scores in three tests (78%),

homework (17%) and class participation (5%). Because homework and class participation

scores are used as independent variables, the percentage points the student earned in in-class

tests is also used as a third dependent variable to define student performance. This way, the study

is able to determine whether homework and class participation scores truly have any positive

effects on student performance.

In addition to the three dependent variables above, 13 independent variables are used for the

regression analysis. The study also uses eight different classification variables for differences in

means test, 16 different variables for one-way analysis of variance, and 25 different variables for

calculating Pearson and Spearman’s correlation coefficients. A list of these variables is presented

below starting with the abbreviation used for each variable in the statistical models and ending

with a definition or an explanation of the variable. The study also explains why some variables

are combined and why some variables were not used in the analysis. The possible responses for

each question (on the survey instrument) representing an independent variable are listed in

brackets

“[ ]”.



Dependent Variables:

1. Letter Grade: The letter grade: A, B, C, D, and F the student earned for the course are

converted to 4, 3, 2, 1, and 0 points respectively.

2. Overall Points in %: The total number of percentage points calculated by giving 78%

weight to three tests, 17% weight to homework based on online homework done on

Connect by McGraw-Hill and extra credit quizzes related to current financial news in The

Wall Street Journal done on Desire 2 Learn (D2L), and 5% weight to students’ class

participation.

3. In-Class Test Score in %: Percentage points the student earned in three tests given in

class. The tests are non-cumulative with 40% weight to two problems and 60% weight to

30 multiple choice questions and up to 10% extra credit for multiple choice questions

based on The Wall Street Journal quizzes.

Independent Variables:

1. GradeMake: The grade I would like to make in the course is [a. an A; b. at least a B

c. at least a C; d. a D is fine with me]. For analysis purpose, it is assumed that A= 4,

B = 3, C=2, and D=1.

2. CFA/CFP: Are you planning to take the Chartered Financial Analyst (CFA) or

Certified Financial Planner (CFP) exam? [a. Yes; b. No; c. Maybe]. For analysis

purpose, it is assumed that Yes =1, No = 0, Maybe = 0. “No” and “Maybe” were

combined into one category because there were only two Nos. The study also

converted what would have been ordinal data into a dummy variable for the

regression analysis purpose.

3. GradSch: Are you planning to attend graduate school? [a. Yes, at this school; b. Yes,

but at another school; c. No; d. Maybe]. For analysis purpose, “Yes at this school,”

frequency 2, with “Yes, but at another school,” frequency 15, were combined and

coded as1. “No,” frequency 2, and “Maybe,” frequency 20, were combined and coded

as 0. It should be noted that the course instructor often discourages students from

going to graduate school right after finishing the undergraduate degree. He also

discourages students from going to graduate school at their current university so that

they can get more diverse educational experience.

4. CHours: In an average week, how many hours do you study for this course?

[____hours].

5. Shours: In an average week, how many hours do you study overall? [____hours]. It

was found that SHours had 0.794 correlation with CHours at 0.000 level of

significance. Thus, Shours was not used in the regression analysis to avoid the

multicollinearity problem. However, high correlation tends to validate the self-

reported CHours by the students.

6. HomeWork: Percentage points earned by a student in online homework assignments

on McGraw-Hill Connect©.



7. Attendance: Percentage of class attendance.

8. ClParticip: Percentage points earned by the student for class participation. It was

found that CPartcip had 0.836 correlation with CAttend at 0.000 level of significance.

Thus, CPartcip was not used in the regression analysis to avoid the multicollinearity

problem.

9. JobHours: In an average week, how many hours do you work at a job outside of

school? [____ hours].

10. JobType: My job outside of school is [a. Finance related; b. Accounting related; c.

Business related (but not finance or accounting); d. Other]. The first three categories

were combined and coded as 1 and the “other” was coded as 0. While this variable

was not used in the regression analysis, because 7 out of 39 values were missing, it

was included in the one-way ANOVA and the correlation tests.

11. CLoad: How many courses are you taking this semester? [____ courses]. The

number of courses reported by the students was verified with data provided by the

University Institutional Research Office (IRO). While student reported numbers were

quite accurate, the data provided by the University IRO was used in the analysis.

CLoad was not included in the regression analysis to avoid the multicollinearity

problem with CrdLoad below.

12. CrdLoad: How many credit hours are you taking this semester? [____ credit hours].

The number of credit hours was verified with data provided by the University IRO.

While student reported numbers were quite accurate, the data provided by the

University IRO were used. CLoad had 0.978 correlation with CrdLoad at 0.000 level

of significance. Thus, CLoad was not used in the regression analysis to avoid the

multicollinearity problem with CrdLoad.

13. Write: My writing ability is [a. Very good; b. Good; c. Average; d. Poor]. For this

variable and the three variables below, the codes used were 4 for Very Good to 1 for

Poor. Also, the order of very good to poor on the survey instrument was scrambled to

diminish the possibility of students marking off the same letter in all four variables.

Moreover, math was put in the middle to reduce the possibility of students marking

writing, reading, and listening abilities the same.

14. Math: My math ability is [a. Poor; b. Average; c. Good; d. Very Good].

15. Read: My reading ability is [a. Poor; b. Average; c. Good; d. Very Good].

16. Listen: My listening ability is [a. Very good; b.Good; c. Average; d. Poor].

17. AvWRL: Due to high (0.41 to 0.62) and significant (0.01 or better) correlation

between Write, Read, and Listen the average of the three variables was calculated and

named it AvWRL. This new variable is highly correlated with each of the three

variables (0.80 to 0.86 with statistical significance of 0.01 or better). It serves as

useful proxy for the three variables and eliminates the multicollinearity problem.



18. FIN350: What was your grade for FIN 350 (Financial Management)? [__ A; ___ B;

____C; ___D]. The grades were verified with data provided by the University IRO.

While student reported numbers were quite accurate, the data provided by the

University IRO was used. As discussed above, grade points of 4, 3, 2, 1 for A, B, C,

and D respectively were used.

19. OGPA: What is your Overall GPA? [___]. Overall GPAs were verified with data

provided by the University IRO. While student reported numbers were quite accurate,

the data provided by the University IRO was used.

20. PMajor: My primary major is [a. Accounting; b. Finance; c. Marketing; d.

Management; e. Other] Since 33 out of 39 students indicated finance as their primary

major, Finance was coded as 1 and all other majors were coded as 0.

21. Gender: Your gender [a. Male; b. Female]. Male was coded as 1 and Female as 0.

22. Age: Your age group [a. 18-22; b. 23-27; c. 27+]. Since 32 out of 39 students were

in the 18-22 age group, this age group was coded 0 and the remaining two groups as

1.

Categorization of Independent Variables

Variables 1, 2, and 3 are classified as motivation factors; variables 4, 6, 7, and 8, as effort

factors; variables 9, 10, and 12 as distraction factors; and variables 14 and 17 as self-perceived

ability factors. Variables 18 and 19 represent prior actual ability and are included for control

purposes and also to determine whether they are associated with student performance. Finally

variables 20, 21, and 22 are included to compare the results of this study with earlier studies

using those variables.

STUDY HYPOTHESES

The study tests one hypothesis for each independent variable, for a total of 14 hypotheses.

To prevent redundancy, all hypotheses are presented in the alternate form only. The formal

statements for these hypotheses are provided below grouped under broad categories of factors:

Motivation Factors

Independent variable number one is GradeMake. It is hypothesized that students who would

like to make higher grades are motivated to perform well and do perform well in the course:

H1: There is a significant positive relationship between the grade the student would like to make

in the Investments course and student performance in that course.

Independent variable number two is whether the student intends to take the CFA or the CFP

exam. It is hypothesized that students who intend to take either of these exams are more

motivated to work hard to learn the material (to increase their chances of passing those exams)

and this leads them to earning higher grade in the course.



H2: There is a significant positive relationship between the student’s intention to take the CFA or

the CFP Exam and student performance in the Investments course.

Independent variable number three under the motivation category is whether the student

intends to attend graduate school. It is hypothesized that students who have that intention are

more motivated to study hard to increase their chances of getting accepted at a good graduate

school, thus they end up earning higher grade in the course.

H3: There is a significant positive relationship between the student’s intention to attend graduate

school and student performance in the Investments course.

Effort Factors A student who is motivated (because of any or all of the three motivation factors

discussed above) to earn a higher grade in the Investments course will spend more hours per

week to study for the course, perform well in all assigned homework, attend all classes, and

participate in class discussions. Thus, the next four hypotheses state that there are significant

relationships between these four effort variables and student performance. Because homework

and class participation scores are used in determining the letter grade for the course and included

in the overall points percentage for the semester, it is mathematically expected that there will be

significant relationship between homework and class participation grades and students’ letter

grade point and overall points percentage score. The third dependent variable, in-class tests

score, does not include homework and class participation grades, and avoids this problem. The

formal hypotheses are stated below:

H4: There is a significant positive relationship between the number of study hours for the

Investments course and student performance in that course.

H5: There is a significant positive relationship between the student’s homework score and

student performance in the Investments course.

H6: There is a significant positive relationship between the student’s class attendance and


H7: There is a significant positive relationship between the student’s class participation grade

and student performance in the Investments course.

Distraction Factors

Independent variable number nine of the study is the average number of hours per week the

student works at a job outside of school. It is hypothesized that students who work more hours

may spend less time studying and doing homework and may attend fewer classes. As a result

they may earn lower grades than students who work fewer hours or those who do not work at all.

H8: There is a significant negative relationship between the student’s average number of

hours of work per week and student performance in the Investments course.

Independent variable number 10 is the student’s job type. It is hypothesized that students

whose job is not related to finance, accounting, or business in general will earn lower grades in

the Investments course than students whose job is related to one of these areas. This is based on

the assumption that the practical experience gained from the job will help students understand

the course material better and thus earn high test scores.

H9: There is a significant negative relationship between the student’s job type (if it is not

related to finance, accounting, or business in general) and student performance in the

Investments course.

Independent variable number 12 is the number of semester credit hours a student is taking. It

is hypothesized that students who are taking more credit hours may spend less time studying per

course and, therefore, will earn lower grades than students who take fewer credit hours.

H10: There is a significant negative relationship between the number of semester credit

hours a student is taking and that student’s performance in the Investments course.



Self-Perceived Ability Factors

Independent variable number 14 represents students’ self-perceived math ability and

independent variable number 17 represents the average of writing, reading, and listening abilities

combined. It is hypothesized that students who perceive their abilities to be higher in these areas

earn higher grades in the Investments course. If students make accurate estimates of their

abilities in these areas and if these abilities affect the performance in the Investments course,

there should be significant positive relationship between these estimates and student

performance. The hypotheses are stated as follows:

H11: There is a significant positive relationship between the student’s self-reported math

ability and student performance in the Investments course.

H12: There is a significant positive relationship between the student’s self-reported writing,

reading, and listening abilities combined and student performance in the Investments

course.

Prior Ability Factors

Independent variable number 18 of the study is the student’s grade in FIN350 (Financial

Management). It is hypothesized that students who earned higher grades in FIN350, which is a

prerequisite for the Investments course, will earn higher grades in the latter course.

H13: There is a significant positive relationship between the grade the student earned in the

Financial Management course and student performance in the Investments course.

Independent variable number 19 of the study is the student’s overall GPA (OGPA). Most

prior research shows significant relationship between GPA and student performance. It is

believed this will be the case in this study as well. So, It is hypothesized that students with higher

overall GPAs will earn higher grades in the Investments course.

H14: There is a significant positive relationship between the student’s overall GPA and


RESEARCH METHODOLOGY

Survey Instrument:

The authors modified a list of survey questions, from Ingram et al. (2002), to include,

besides the study variables, some demographic and other information. For ethical,

confidentiality, and potential risk issues pertaining to participants, the authors had to submit a

comprehensive 10-page application (together with a copy of the survey instrument) to the

University’s Institutional Review Board (IRB) for approval. Prior to that, both authors had to

take the National Institute of Health (NIH)’s training course titled “Protecting Human Research

Participants,” and pass the test given at the end of the course. The certificates of completion of

the course were required to be submitted with the application to the University’s IRB. The

University’s IRB made only one modification to the survey instrument by adding the statement

that “participation in the survey is completely voluntary.”

Study Sample:



In fall 2010, the data on the survey instrument were collected from 41 of 51students

enrolled in the two sections of the undergraduate Investments course offered at a public

residential school. Only two sections of the Investments course were offered and both sections

were taught by the same instructor, so instructor effect is not an issue in this study. The

university enrolls about 10,000 students, and the College of Business enrolls about 1,600

students. It is a state-owned university that has public access as a major part of its mission

statement. It is located near some of the largest cities in the United States. It is one and a half

hour drive from Philadelphia and two-hour drive from New York City. The final sample

included 39 useful responses as one student dropped the course after filling out the questionnaire.

Another student’s response was dropped from the sample because there was some doubt about

the truthfulness of the response. The student’s performance in the course was very poor despite

very high reported hours of study. While it is possible that academically poor students may spend

more hours studying and still earn lower grades, the gap was so wide in this case that it was felt

that the observation was an outlier and may affect the mean for tis variable and possibly other

variables as well. The instructor teaching the course provided us (using only students’ ID

numbers for confidentiality purposes) with the data representing the three dependent variables

(the “letter grade,” “overall points,” and “in-class tests scores”) and three independent variables

(homework, class participation, and attendance percentage points.)

Table 1 presents descriptive statistics of the sample variables. Two different graduate

students entered the data from student questionnaire on two separate Excel spreadsheets. The

authors matched the two spread sheets and resolved any discrepancy by referring to original

questionnaire. This virtually eliminated any data entry errors. The students’ provided data

related to the number of semester courses, semester credit hours, FIN350 grade, and overall GPA

were verified with official data (again using only students’ ID numbers for confidentiality

purposes) supplied by university’s IRO. Students’ self-reported data were quite accurate. This

provided great confidence that non-verifiable data provided by the students should be accurate as

well.

Data Analysis:

To test the formulated hypotheses, one-way analysis of variance (ANOVA), Pearson and

Spearman’s correlation coefficients and ordinary least square regression analysis were used.

STUDY RESULTS

Table 1 presents the minimum and maximum value, the mean, and the standard deviation

for each of the 19 non-binary variables of the study. That Table shows an average grade in the

course of only 2.31versus 3.21 in the Financial Management course which is a pre-requisite for

the course. It is also much lower than overall GPA of 3.11, and average Grade Make of 3.56. In

comparison, Didia and Hasnat (1998) study of performance determinants in a finance course

report a Financial Management course GPA of only 1.85, GPA in a pre-requisite course of 2.71,

and overall GPA of 2.61. It is interesting to note that the difference between average course

letter grade and pre-requisite course(s) GPA of 0.90 is comparable to that of Didia and Hasnet of

0.86. Also, the difference between average course letter grade and overall GPA of 0.80 is

comparable to that of Didia and Hasnet of 0.76. No comparable data is available for the

difference between average actual grade point in the course and average Grade Make points.

Students’ self-reported average study time for the course is 3.01 hours per week which is

only one-half the 6 hours per week recommended by the instructor both verbally and in the

syllabus. In comparison, Didia and Hasnet (1998) report 3.91 hours per week of study time for



financial management classes they studied. Students’ self-reported average total study time for

all courses is only 11.79 hours per week compared to the suggested 30.82 hours per week based

on average of 15.41 semester credit hours course load and the recommended two hours per

semester credit hour study time. This is even lower than the average Business Majors study time

of 13.14 hours for 2004 reported by Babcock and Marks (2011). If the average reported total

study hours of 11.79 per week and is added to the job hours of 16.19 per week the total would be

27.98 hours, which comes very close to the recommended study hours of 30.82 per week.

Babcock and Marks (2011) show decline in studying hours by non-working students also.

However, if instructors lower the course rigor to meet the needs of the majority of working

students, non-working students will not find it necessary to study more to achieve their academic

objective of achieving higher grade. It appears that students’ need to work is cutting into their

recommended study hours.

Table 2 presents differences in means tests for selected variables. Table 2 shows male to

female ratio in the class of 69% to 31% with males earning an average of 0.33 higher letter grade

points, 2.3 higher overall points score, and 3.3 higher in-class tests score percentage. The

differences are statistically insignificant. The OLS regression (which is not shown here) also has

positive but insignificant coefficient for the males over females. Didia and Hasnet (1998) on the

other hand find negative but insignificant coefficient for males. Two other studies find the role

of gender in the finance course performance to be statistically significant, however they show

opposite results. Sen et al. (1997) show significant negative coefficient for females, while

Henebry and Diamond (1998) show significant positive difference in grade for females.

With regard to age, Table 2 shows that 18% of the students were above the age of 22

years earning an average of 0.55 lower letter grade points, 5.2 lower overall points percentage,

and 2.4 lower in-class tests score percentage. All the results are statistically insignificant. This

is opposite of Didia and Hasnet (1998) which finds positive and significant coefficient for the

actual age variable in the OLS regression.

Table 2 also shows that 85% of the students taking the Investments course were finance

majors and averaged 0.95 higher letter grade points, 9.3 percentage higher overall points score,

and 10.6 percentage point higher in-class tests score than other majors. Letter grade points and

in-class test score differences were significant at the .10 level of significance.

We now analyze the results of the study by the type of factors investigated (motivation,

effort, distraction, self-perceived abilities, and prior ability).

Motivation Factors Associated with Student Performance:

Of the three motivation variables discussed in H1 to H3, GradeMake is significantly

associated with student performance (however defined) based on One-Way ANOVA, and

Pearson and Spearman’s Correlation Coefficients at .01 level of significance (Tables 3 and 4).

Table 5 shows that after controlling for prior ability, as measured by OGPA and FIN 350,

GradeMake is still correlated with overall points and in-class tests score at the .01 level of

significance but at only the .05 level of significance with letter grade. Table 6 shows two

different models of OLS regression for each of the three dependent variables. Model 1 is a full

model that includes all the independent variables and Model 2 excludes Homework and Class

Participation because they are included in the calculation of the letter Grade and overall total

points. Again, Grade Make is significant at the .01 level for Model 2, the better model, for all

three measures of performance. These results are consistent with Paulsen and Gentry (1995),

Wooten (1998), Maksy and Zeng (2008) and others.

Intention to take the CFA or the CFP exam (CFA/CFP) is significantly associated (but

only at the .10 level of significance) with student performance as measured by overall points



score according to ANOVA (Table 3) and as measured by the overall points score or in-class

tests score according to Pearson and Spearman correlations (Table 4). However, the correlation

coefficients become insignificant when the prior ability factors are controlled for as shown in

Table 5. Also, using the OLS regression, CFA/CFP is found to be not significantly associated

with any of the three performance measures under the two models shown in Table 6.

The third motivation factor, intention to attend graduate school, shows no significant

association with student performance using ANOVA (Table 3), correlations (Tables 4 and 5), or

OLS regression (Table 6) when using overall points or in-class tests score to measure student

performance. However, it has significant negative association at the .10 level of significance

(Model 1) and at the .05 level (Model 2) when using the letter grade as the measure of student

performance. This may be considered as an anomalous result in view of Table 2 showing that

41% of students who intended to go to graduate school earned 0.12 higher letter grade than

students who did not intend to go to graduate school. The .12 difference is not statistically

significant.



Effort Factors Associated with Student Performance:

Of the four effort variables discussed in H4 to H7, the number of study hours for the

course (CHours) is statistically insignificant in explaining student performance according to both

ANOVA and Pearson and Spearman correlations (Tables 3 and 4). It is correlated at the .10

level of significance with overall points when the prior ability factors are controlled for (Table

5). Table 6 indicates that the OLS regression analysis shows that CHours is significantly related

to performance at the .10 level of significance for all three measures of performance under

Model 1, at the .05 level (when performance is measured as in-class tests score,) and at the .01

level (when performance is measured as total points) under Model 2. This result of this study is

consistent with Paulsen and Gentry (1995), and Johnson, Joyce and Sen (2002) but inconsistent

with Didia and Hasnat (1998) and Nosfinger and Petry (1999).

Student performance on online homework assignments has significant relationship with

student performance. The association is significant at .10 when student performance is measured

as the letter grade or in-class tests score (according to ANOVA in Table 3) and at .01 when

student performance is measured as overall points % (according to ANOVA, Pearson and

Spearman correlations in Tables 3 and 4). This is consistent with Wooten (1998). After

controlling for the prior ability factors, the significant association between performance in

homework assignments and in-class tests disappears, but continues with the letter grade at the .05

level and with overall points at the .01 level (Table 5). Similar result is obtained from the OLS

regression where no significant association is found between homework and the letter grade and

in-class tests but there is significant association (at the .01 level) between homework and the

overall points score.

Class attendance has varied significant associations with student performance depending

on the definition of performance. The significance level is .10, .05 and .01 when student

performance is measured as in-class tests score, letter grade, or overall points respectively as

shown in ANOVA in Table 3. This again is consistent with Wooten (1998). However, as Table

4 indicates, class attendance is significant at the .10 significance level with the letter grade and at

the .05 level with overall points under Pearson correlations and at the .10 level with the letter

grade under Spearman correlations. As Table 5 indicates, after controlling for the prior ability

factors, the significant associations between class attendance and all three measures of

performance disappear. The OLS regression analysis shows no significant associations between

class attendance and student performance, however defined, under both Models of Table 6.

As Table 3 indicates, students’ class participation grades show no significant association

with any of the three performance measures according to the ANOVA tests. However, as Table

4 indicates, both Pearson and Spearman correlations show significant associations at the .01 level

when performance is measured as the letter grade or overall points and at the .05 level when

performance is measured as the in-class tests score. This is consistent with Rich (2006). As Table

5 indicates, after controlling for the prior ability factors, the associations between class

participation and all three measures of performance disappear. As Table 6 indicates, the authors

obtain similar results from the OLS regression where no significant associations are found

between class participation and all three measures of performance.

Distraction Factors Associated with Student Performance

As Table 3 indicates, none of the four distraction factors, Job Hours, Job Type, Number

of Semester Courses, or Semester Credit Hours shows any significant association with any of the

three measures of student performance based on ANOVA tests. As Table 4 indicates, Pearson

correlations show significant (at .05) association between Number of Courses and Number of

Credits and student performance measured as letter grade or overall points and at .10 when



performance is measured as in-class test scores. This appears to be consistent with Didia and

Hasnat (1998). Spearman correlations show significant association (at the .10 level of

significance) between Number of Credit Hours and student performance measured as the letter

grade. After controlling for the prior ability factors, none of the four distraction factors has any

significant association with any of the three measures of student performance. Similarly, as

Table 6 indicates, the OLS regression analysis shows no significant association between either

Job Hours or Semester Credit Hours and any of the measures of student performance. The

results with regard to Job Hours are consistent with Wooten (1998) who found that work

responsibility did not affect student performance in an Introductory Accounting course. The Job

Type and Course Load were not included in the OLS regression analysis for the reasons

explained earlier.

Table 7, Part A, indicates that each distraction factor has no significant negative effect on

student performance (however defined) even when the other two distraction factors are

controlled for. Table 7, Part B, indicates that controlling for the other two distraction factors as

well as the two prior actual ability variables (FIN350 and OGPA), the results remain the same.

Self-Perceived Abilities Factors Associated with Student Performance

Of the four self-perceived abilities factors, Math, Writing, Reading, and Listening, only

Math has statistical significant association with student performance (however defined) at the .05

level of significance according to ANOVA (Table 3) and Pearson and Spearman Correlations

(Table 4). After controlling for the prior ability factors, even Math becomes not significantly

correlated with student performance. However, as Table 6 indicates, according to OLS, Math

has significant association with student performance. That association is significant at the .10

level when performance is defined as the letter grade or the in-class tests score under Model 1

and at the .05 level when performance is defined as overall points under both Models, or as in-

class tests score under Model 2. This is consistent with Grover et al. (2010) and Didia and

Hasnat (1998). Average of Writing, Reading and Learning self-perceived abilities combined

does not have any significant association with student performance under any statistical test.

Prior Actual Ability (Control) Factors Associated with Student Performance

The ANOVA tests show significant association between the FIN 350 course grade and

student performance. The association is significant at the .05 level when performance is

measured as the overall points or the in-class tests and at the .01 level when performance is

measured as the letter grade. However, the association between OGPA and student performance

is statistically insignificant even though its F-statistic is similar to that of the FIN 350 course

grade. Since OGPA is a continuous variable rounded to two decimal places it has 35 categories

or cells resulting in df1=35 and df2=3 and higher critical value of F-statistic. This situation

reverses itself in the OLS regression analysis in Table 6 where OGPA coefficient is significant at

the .10 level for the letter grade and overall points and at the .05 level for in-class tests score.

This is perhaps the case because OLS models work better with the continuous variable OGPA.

In OLS, FIN 350, a discrete variable, is insignificant under both Models 1 and 2 for all the three

performance measures. According to Pearson and Spearman correlations (Table 4), both FIN

350 and OGPA have significant associations at the .01 level of significance with all three

measures of performance. These results are consistent with numerous studies mentioned in the

literature review. Also, these significant associations make the use of these two variables for

control purposes an appropriate procedure.



CONCLUSIONS AND RECOMMENDATIONS

One general conclusion of the study is that motivated students perform better in the

Investments course than non-motivated students. More specifically, all the tests used in the study

provided strong evidence that students who responded that they wanted to make higher grades in

the Investments course ended up earning higher grades. However, Table 1 shows that there was

quite a disparity between average Grade Make of 3.56 and average letter grade of only 2.31.

Also, speaking of motivation, ANOVA and Pearson and Spearman Correlations provide

moderate to week evidence that intention to take the CFA or the CFP examination is a

motivating factor for students to perform well in the Investments course. However, intention to

attend graduate school does not seem, in this study, to be a motivating factor for the students to

perform well in the Investments course.

In light of the above general conclusion, it is recommended that finance faculty should

encourage their students to plan to take the CFA or the CFP exam, and design courses that help

them prepare for CFA and CFP. This may motivate them to study hard and to do well in the

Investment course.

Another general and logically expected conclusion of the study is that students who

spend more time studying for the Investments course earn better grades than those who spend

less time.

A related and logically expected general conclusion of the study is that students who do

well in their homework earn better test and overall grades than students who do not do as well in

their homework. The ANOVA and correlation tests provide strong to moderate evidence in

support of this conclusion. On the other hand, the OLS regression analysis shows that

Homework’s association with student performance is insignificant, and this does not support the

above conclusion. However, this paradox may have to do with the statistically significant

correlation between student performance and GradeMake and OGPA. Both GradeMake and

OGPA have statistically significant coefficients resulting in reducing or eliminating the direct

significance of Homework Grades in the OLS regression models. Significant correlations

between Homework and GradeMake (Table 4) show expected relationships between motivation

and effort. This is consistent with Wooten (1998) who finds significant relationship between

motivation and effort. One of the variables Wooten uses to measure effort is homework

submissions.

Although Class Attendance shows strong association with student performance

(particularly when it is measured by the letter grade or overall points) in accordance with the

ANOVA tests, evidence based on correlation coefficients, in accordance with correlations tests,

is weaker. Controlling for OGPA and FIN 350 grades, the correlation between attendance and

course performance variable is negative but insignificant. OLS coefficients for the Attendance

variable are also generally negative but statistically insignificant. Again Attendance has

significant positive correlation with Grade Make and also with OGPA. This again shows

expected relationship between motivation and effort similar to Wooten (1998).

Class Participation shows statistically insignificant association with overall points

performance in course and tests based on ANOVA tests. Evidence based on correlation

coefficients is much stronger. After controlling for OGPA and FIN 350 grades, the correlation

between class participation and course performance becomes negative but insignificant. The

OLS coefficients for the Class Participation variable are also statistically insignificant. Again,

Class Participation has significant positive correlation with Grade Make and also with OGPA.

This again shows expected relationship between motivation and effort.

In light of the above discussion regarding Course Study Hours, Homework Grades, Class



Attendance, and Cass Participation, it can be concluded that there is direct significant association

between effort and course performance based on at least one of the four statistical tests. In

addition, there are associations between the effort factors (Homework Grades, Class Attendance,

and Class Participation) and the motivation factor, Grade Make, and also the prior ability factor,

OGPA. There is also a significant correlation between Grade Make and OGPA and the grade in

FIN 350 at the .01 level of significance. One possible explanation for all this is that students

with prior ability are highly motivated to achieve higher grades and put effort reflected in

Homework Grades, Attendance and Class Participation. However, these students do not have to

put in more study hours for the course which is reflected in statistically insignificant association

between Course Study Hours and performance in the Course (letter grade and in-class test

scores) according to both ANOVA tests and correlation tests (Tables 3 and 4). Only when the

other variables are controlled for, which OLS does, the Course Study Hours coefficient is found

to be significant at the .10 level of significance. Therefore, it can be concluded that, everything

else being the same, more studying can improve students’ performance in test scores and overall

points score.

In light of the above discussions, it is recommended that finance and accounting faculty

inform their students that research shows that these activities do indeed improve students’

grades.

An initial conclusion from the statistical tests of this study is that the distraction

variables, i.e. number of hours of work per week, working in non-finance, accounting, or

business related job, number of course credit hours taken in the semester have no statistical

significant negative associations with student performance. That is, they do not distract the

students and prevent them from earning higher grades in the Investments course. This is

consistent with Chan et al. (1997), Wooten (1998), and others.

However, upon a closer look, Table 2 shows that 41% of the students (those who are

working 20 hour or more per week) earned 0.30 less letter grade points, 5.0 less in overall points

percentage, and 2.8% less in in-class tests score than students who are working less than 20

hours per week. Similarly, 45% of the students (those who are working in non-finance,

accounting, or business related jobs) earned 0.33 less letter grade points, 4.5 less in overall

points, and 3.9% less in in-class tests score than those who have accounting, finance, or business

related jobs. While these numbers are not statistically significant, they cannot be completely

ignored.

Moreover, Job Hours show significant negative Pearson correlation with Homework

Grade, Attendance, Course Study Hours, and Semester Credit Hours (Table 4). These results are

consistent with Paisey and Paisey (2004) and Lynn and Robinson- Backmon (2005). The first

three of these may have negative consequences for learning outcomes not reflected in course in-

class tests, overall points, or letter grade. The last one could delay graduation.

Surprisingly, 44% of the students (those taking 6 or more courses in the semester) earned

0.39 higher letter grade points, 4.2% higher overall points percentage, and 4.0% more in-class

tests percentage. However, none of those are statistically significant. Pearson correlations show

significant correlations between Number of Credit Hours and letter grade and overall points (at

the .05 level of significance) and in-class tests (at .10% level of significance). However, after

controlling for the prior ability factors, the correlations between Number of Credit Hours and

performance measures are small and insignificant (Table 5). This may indicate that better

students take more courses. This conjecture is confirmed in Table 4 which shows significant

high correlation between Course Credit hours and OGPA at 5% or better level of significance.

Moreover, per Table 6, while the OLS regression results show that Course Credit Hours

coefficients are negative, they are not statistically significant, under both Models, all across the

three measures of student performance.



In light of these conclusions, it is recommended that students be encouraged to work

fewer than 20 hours per week so that they can earn better grades and graduate sooner.

A fourth general conclusion of the study is that students’ estimate of their own math

ability has significant association with students’ performance in the Investments course. The

statistically significant OLS coefficients for Math show that, everything else remaining the same,

students with better Math ability perform better in the Investment course. Students’ self-reported

writing, reading, and listening abilities are not significantly associated with student performance

in the course.

In light of this general conclusion, it is recommended that the college of business faculty

in general, and finance faculty in particular, should encourage students with better math abilities

to major in finance and students interested in the Finance major be encouraged to assess and

develop their math abilities.

As expected and as shown in prior studies with respect to other courses, a fifth general

conclusion of the study is that students with high prior actual ability end up earning high grades

in the Investments course. Specifically, the study provides strong evidence that students’

performance in FIN350 and their OGPA, are strong predictors of their performance in the

Investments course.

In light of this general conclusion, it is recommended that accounting and finance faculty

encourage their students to study hard and improve their GPA by emphasizing that research

shows that students with high overall GPA continue to earn high grades in the Investments

course. Again, it must be realized that some faculty may already be doing this; thus, these

recommendations are for those who may not be.

STUDY LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH

This study is subject to some limitations. One limitation is that the study school is a

public (or state-supported) university and, thus, the conclusions may not be applicable to private

schools. One suggestion for further research is to replicate the study at a private school. Another

limitation is that the study school is a residential school with most students enrolling in the

Investments course in their early 20s and it is possible that the results may not be generalizable

to commuter schools with generally older students. Consequently, another suggestion for further

research is to replicate the study at a commuter school with older students. A third limitation is

that the study sample is somewhat small relative to the number of variables analyzed and, hence,

the results may not be as robust as they would have been if the sample was larger. Thus, another

suggestion for further research is to replicate the study using a somewhat larger sample by

collecting data over a number of years if class size is small.

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TABLES

Table 1

Descriptive Statistics for the Study Non-binary Variables

N Minimum Maximum Mean Std.

Deviation

Letter Grade1 39 0 4 2.31 1.20

Overall Points % 39 39 94 76.85 12.72

In-Class Tests in % 39 48 94 75.26 12.63

FIN 350 Grade1 39 1 4 3.21 0.92

OGPA 39 2.04 4.00 3.11 0.51

Grade Make1

39 2 4 3.56 0.68

Home Work Grade in % 39 5 100 82.90 23.94

Attendance in % 39 46 100 84.03 16.66

Class Participation Grade in % 39 23 80 56.74 14.97

Course Study Hours 39 0.50 8.00 3.01 1.59

Total Study Hours 39 0.50 25.00 11.79 6.65

Job Hours 39 0.00 52.0 16.19 12.64

Number of Courses 39 2 7 5.21 1.03

Number of Credit Hours 39 6 18 15.41 2.92

Math Ability2

39 1 4 3.44 0.72

Writing Ability2

39 1 4 3.15 0.78

Reading Ability2

39 1 4 3.10 0.85

Listening Ability2

39 1 4 3.18 0.76

Average of Write/Read/Listen 39 1.33 4.00 3.15 0.66 1A = 4.00; B = 3.00; C = 2.00; D = 1.00; F = 0.00

2Very Good =4; Good =3; Average =2; Poor =1



Table 2

Differences in Means Tests for Selected Variables

Variable Category Number

(%)

Mean Letter

Grade

Mean Overall

Points %

Mean In-

Class Tests

%

Gender

Male 27 (69) 2.41 77.6 76.3

Female 12 (31) 2.08 75.3 73.0

Male – Female 0.33 2.3 3.3

P-Value 0.442 0.608 0.465

Age

Above 22 7 (18) 1.86 72.6 73.3

18-22 32 (82) 2.41 77.8 75.7

Difference -0.55 -5.2 -2.4

P-Value 0.277 0.333 0.655

Primary

Major

Finance 33 (85) 2.45 78.3 76.9

Other 6 (15) 1.50 69.0 66.3

Finance – Other 0.95 9.3 10.6

P- Value 0.071* 0.101 0.059*

CFA/CFP

Yes 161 (41) 2.63 81.1 79.1

No/Maybe 23 (59) 2.09 73.9 72.6

Yes – No/Maybe 0.54 7.2 6.5

P-Value 0.170 0.079* 0.112

Grad

School

Yes 161 (41) 2.38 79.0 76.7

No/Maybe 23 (59) 2.26 75.4 74.3

Yes – No/Maybe 0.12 3.6 2.4

P-Value 0.774 0.385 0.562

Job Hours

20 hours or more 16 (41) 2.13 73.9 73.6

Less than 20 hours 23 (59) 2.43 78.9 76.4

Difference -0.30 -5.0 -2.8

P-Value 0.433 0.276 0.492

Job Type

Other 14 (45) 2.14 74.6 73.9

Acc-Fin-Bus Rel. 17 (55) 2.47 79.1 77.8

Difference -0.33 -4.5 -3.9

P-Value 0.467 0.370 0.411

Course

Load

6 or more courses 17(44) 2.53 79.2 77.5

Fewer than 6

courses 22 (56) 2.14 75.0 73.5

Difference 0.39 4.2 4.0

P-Value 0.315 0.321 0.343

*Significant at 10% level of significance using two tails test 1Only 10 students indicated preference for both CFA/CFP and Graduate school



Table 3

One-Way Analysis of Variance

(All numbers are for Between Groups Only)

Complete ANOVA Numbers are Available from the Authors upon Request

Dependent Variables

Letter Grade Overall Points % In-Class Tests %

Indep. Var. DF F Sig. F Sig. F Sig.

Grade Make 2/36 19.778 0.000*** 28.314 0.000*** 20.162 0.000***

CFA/CFP 1/37 1.960 0.170 3.254 0.079* 2.655 0.112

Grad School 1/37 0.084 0.774 0.774 0.385 0.342 0.562

Course Study

Hours 11/27 0.243 0.991 0.224 0.994 0.282 0.984

Home Work 20/18 2.062 0.064* 3.715 0.004*** 1.850 0.097*

Attendance 11/27 2.451 0.028** 3.043 0.009*** 1.855 0.093*

Class

Participation 21/17 1.318 0.284 1.844 0.102 1.045 0.469

Job Hours 16/22 0.673 0.790 0.691 0.773 0.485 0.929

Job Type 1/29 0.544 0.467 0.830 0.370 0.696 0.411

Course Load 4/34 1.441 0.242 1.945 0.125 1.449 0.239

Credit Load 5/33 1.302 0.287 1.481 0.223 1.177 0.341

Math 3/35 3.235 0.034** 3.236 0.034** 4.072 0.014**

Write 3/35 0.218 0.883 0.367 0.777 0.087 0.967

Read 3/35 0.104 0.957 0.234 0.872 0.052 0.984

Listen 3/35 1.587 0.210 0.868 0.467 1.744 0.176

AvWRL 7/31 1.297 0.285 1.496 0.205 1.209 0.327

FIN350 3/35 4.982 0.006*** 4.039 0.014** 3.506 0.025**

OGPA 35/3 4.569 0.117 3.357 0.173 4.302 0.127

*Significant at 10% level of significance using two tails test

**Significant at 5% level of significance using two tails test

***Significant at 1% level of significance using two tails test

Journal of Finance and Accountancy Volume 16 – September, 2014

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